The contemporary system of drug discovery, development and approval (DDDA) is highly specific and target-directed. The system is built on a model of intended biochemical and molecular actions. New compounds (i.e., chemical entities or biological factors) are identified, optimized and evaluated based on their actions on intended therapeutic targets. These intended therapeutic targets are typically proteins or genes believed to be involved in a disease process of interest (the drug target). Increasingly, other cellular macromolecules are also becoming important as drug targets (e.g., mRNA).
Actions on the drug target are evaluated against large numbers of compounds by use of high-throughput screening (HTS) assays that measure the activity or state of the protein or gene target. Compounds showing potentially useful activity on the drug target are termed lead compounds (also known as “drug leads”). Once identified, drug leads are filtered and selected on the basis of their activity on the disease process targeted and, ultimately, on clinical end-points. FDA approval is ultimately given for single, well-defined clinical indications that are identified and defined and tested in advance in specified diseases.
Drug leads are therefore both discovered and developed in the context of a highly constrained set of protocols built on a model of intended actions. Drug targets are specific and are identified in advance for discovery initiatives. Stated differently, FDA approval of a drug lead is not obtained by administration of the compound to diverse people with a variety of random medical disorders to see if it helps one or more of these, but occurs within an explicit context of prospectively defined effects in specific disease states.
This approach of contemporary DDDA has major flaws. First, identifying “hits” by HTS assays against molecular targets hypothesized to be involved in a disease does not in fact establish or prove activity against the disease. Activity against disease still has to be validated independently. Indeed, true in vivo activity and efficacy of drug leads may be unreliably or misleadingly assessed by HTS molecular assays. Second, subsequent validation of drug leads against physiologic models of disease often remains highly inefficient for the chronic conditions that are the major therapeutic targets of current drug research, thereby leading to a downstream roadblock in the DDDA system in the filtering steps. Third, unintended toxic actions of drugs are not identified by this approach. Fourth, unintended or secondary therapeutic actions (i.e., other actions besides the effects on the specific molecular target screened) also are not identified efficiently by this approach, thereby missing out on the detection and discovery of other potential therapeutic uses of compounds on which a pharmaceutical company is already investing time and money to develop. Fifth, synergistic effects of combination therapies on a disease process, occurring by interactions among compounds that act on different biochemical steps in the disease pathway, are not detectable by screening approaches which only measure one step in a disease pathway at a time.
The ideal solution to the problem of unintended actions and functional importance of compounds discovered through screening for specific, intended molecular actions is therefore evident: a systematic method for measuring and identifying unintended actions and functional consequences of compounds, combinations of compounds, and mixtures of compounds. The availability of a systematic procedure for efficient, high-throughput discovery and confirmation of unintended actions of compounds or combinations of compounds or mixtures of compounds on functionally relevant biological processes would therefore radically alter the entire DDDA process. Such methods are disclosed herein, in addition to methods for screening and comparing compounds and combinations of compounds and mixtures of compounds for actions on intended processes.